Contents
What GPU do I need to train BERT?
For most of the fine-tuning experiment in the BERT paper, you need more than 16GB GPU memory for BERT-Large. All the mini-batch training assigned to a GPU must fit inside the GPU memory all at once. For example, for a mini-batch size of 64, you cannot split it up into two and later combine the results.
Is GPU required for BERT?
BERT is a huge model, more than 100 million parameters. Not only we need a GPU to fine tune it, but also in inference time, a CPU (or even many of them) is not enough. It means that if we really want to use BERT everywhere, we need to install a GPU everywhere.
How much VRAM do you need to train a BERT?
If you’re generally doing NLP(dealing with text data), you don’t need that much of VRAM. 4GB-8GB is more than enough. In the worst-case scenario, such as you have to train BERT, you need 8GB-16GB of VRAM.
How long is a BERT trained?
Timeline of BERT training records: NVIDIA uses 1,472 NVIDIA V100 GPUs and cuts down the typical training time for BERT-Large from several days to just 53 minutes.
Can CPU run BERT model?
Can I run the server side on CPU-only machine? ¶ Yes, please run bert-serving-start -cpu -max_batch_size 16 . Note that, CPU does not scale as good as GPU on large batches, therefore the max_batch_size on the server side needs to be smaller, e.g. 16 or 32.
How much does it cost to train a BERT?
BERT-large was trained on 64 TPU chips for four days at an estimated cost of $7,000. Grover was trained on 256 TPU chips for two weeks, at an estimated cost of $25,000.
How long does it take to train a Bert model?
BERT has released a number of pre-trained models. Most NLP researchers will never need to pre-train their own model from scratch. Fine-tuning is inexpensive. One can replicate all the results given in the paper, in at most 1 hour on a single Cloud TPU, or a few hours on a GPU.
What is the output of the Bert model?
What is the output of Bert? The output of Bert model contains the vector of size (hidden size) and the first position in the output is the [CLS] token. Now, this output can be used as an input to our classifier neural network for classification of the toxicity of the words.
Are there different types of Bert pre trained?
There are four types of pre-trained versions of BERT depending on the scale of the model architecture: Fun fact: BERT-Base was trained on 4 cloud TPUs for 4 days and BERT-Large was trained on 16 TPUs for 4 days! For details on the hyperparameter and more on the architecture and results breakdown, I recommend you to go through the original paper.
How does training the Bert loss function work?
While training the BERT loss function considers only the prediction of the masked tokens and ignores the prediction of the non-masked ones. This results in a model that converges much more slowly than left-to-right or right-to-left models.